BestHub
Sign in
Tagged articles

reinforcement learning

822 articles · Page 5 of 9
Data Party THU
Data Party THU
Sep 19, 2025 · Artificial Intelligence

How DeepSeek R1 Redefines AI Reasoning with Pure Reinforcement Learning

DeepSeek R1 replaces traditional supervised fine‑tuning with a pure reinforcement‑learning pipeline, introducing the GRPO algorithm and a four‑stage training regime that dramatically lowers cost, boosts reasoning and code‑generation performance, and raises important ethical, privacy, and societal considerations for large language models.

AI reasoningDeepSeekGRPO
0 likes · 14 min read
How DeepSeek R1 Redefines AI Reasoning with Pure Reinforcement Learning
HyperAI Super Neural
HyperAI Super Neural
Sep 19, 2025 · Artificial Intelligence

Weekly AI Paper Roundup: RL Advances, Tree‑Structured QA, and GraphRAG Breakthroughs

This article surveys five recent AI papers, covering reinforcement learning for large reasoning models, a tree‑structured table QA framework (ST‑Raptor), visual representation alignment for multimodal LLMs, GraphRAG‑based generation, and an LLM‑driven cryptographic vulnerability detector, each with key insights and links.

Large Language ModelsMultimodal LLMcryptographic vulnerability detection
0 likes · 5 min read
Weekly AI Paper Roundup: RL Advances, Tree‑Structured QA, and GraphRAG Breakthroughs
DataFunSummit
DataFunSummit
Sep 18, 2025 · Artificial Intelligence

Boosting LLM Function Call: Data, Training, and Agent Optimization Strategies

This presentation by Yao Yitong of China Telecom AI Research Institute explains why Function Call is essential for LLM deployment, outlines data‑centric and training‑centric optimization methods, discusses common pitfalls and reward‑function design for reinforcement learning, and showcases practical Agent application patterns for real‑world tasks.

AgentData ConstructionFunction Call
0 likes · 36 min read
Boosting LLM Function Call: Data, Training, and Agent Optimization Strategies
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 14, 2025 · Artificial Intelligence

How MM‑DREX Uses Multimodal LLMs for Dynamic Expert Routing in Financial Trading

The article reviews the MM‑DREX framework, which tackles the non‑stationarity of financial markets by modeling trading as a POMDP, employing a vision‑language model‑driven dynamic router to allocate four heterogeneous experts, and demonstrates superior returns, Sharpe ratios, and drawdown control across stocks, futures, and crypto compared with 15 strong baselines.

Dynamic RoutingLLMPOMDP
0 likes · 13 min read
How MM‑DREX Uses Multimodal LLMs for Dynamic Expert Routing in Financial Trading
Fighter's World
Fighter's World
Sep 12, 2025 · Artificial Intelligence

Why Are Production‑Grade AI Agents So Hard to Build?

The article analyses why production‑grade AI agents remain unreliable, pinpointing the scarcity of high‑quality task‑action data, the limits of static benchmarks, and the need for massive data‑generation engines, simulation sandboxes, sophisticated RL reward design, and efficient context engineering.

AI AgentContext EngineeringData Generation
0 likes · 21 min read
Why Are Production‑Grade AI Agents So Hard to Build?
DataFunTalk
DataFunTalk
Sep 12, 2025 · Artificial Intelligence

Key Takeaways from AI Leaders at the 2024 Inclusion·Bund Conference

The 2024 Inclusion·Bund conference gathered top AI pioneers—including Turing laureate Richard Sutton, Alibaba Cloud founder Wang Jian, HKU professor Ma Yi, Yushu Tech CEO Wang Xingxing, and historian Yuval Harari—to discuss the limits of intelligence, the shift toward open‑source resources, embodied AI, and the societal implications of rapid AI advancement.

AIArtificial Intelligencereinforcement learning
0 likes · 15 min read
Key Takeaways from AI Leaders at the 2024 Inclusion·Bund Conference
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 11, 2025 · Artificial Intelligence

Fin-PRM: Alibaba’s Dianjin Team Introduces a Domain-Specific Process Reward Model for Financial Reasoning

Fin‑PRM, a domain‑specific process reward model for financial reasoning introduced by Alibaba’s Dianjin team, employs dual‑level step and trajectory rewards to provide fine‑grained supervision, achieving up to 12.9% accuracy gains in supervised fine‑tuning and 5.1% improvements in Best‑of‑N inference on benchmarks such as CFLUE and FinQA.

CFLUEFin-PRMFinQA
0 likes · 11 min read
Fin-PRM: Alibaba’s Dianjin Team Introduces a Domain-Specific Process Reward Model for Financial Reasoning
Instant Consumer Technology Team
Instant Consumer Technology Team
Sep 11, 2025 · Artificial Intelligence

How REFRAG Cuts LLM Decoding Time by 30×: A New Efficient RAG Framework

REFRAG (REpresentation For RAG) introduces a novel decoding framework that compresses, senses, and expands context using precomputed chunk embeddings, achieving up to 30.85× faster first-token generation and 16× larger context windows without sacrificing perplexity, as validated across diverse long‑context tasks.

LLMRAGchunk embeddings
0 likes · 18 min read
How REFRAG Cuts LLM Decoding Time by 30×: A New Efficient RAG Framework
xkx's Tech General Store
xkx's Tech General Store
Sep 10, 2025 · Artificial Intelligence

Exploring WebDancer: Alibaba’s WebAgent that Solves Complex Queries Automatically

This article walks through installing Alibaba's WebDancer agent, explains its SFT‑plus‑RL training pipeline—including data construction, trajectory sampling, supervised fine‑tuning, and reinforcement learning—compares it with the earlier WebWalker, and demonstrates its multi‑step reasoning on a real‑world query.

AI AgentAlibabaLLM agents
0 likes · 10 min read
Exploring WebDancer: Alibaba’s WebAgent that Solves Complex Queries Automatically
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 5, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Aug 30 – Sep 5, 2025)

This digest reviews four recent AI‑driven finance papers: a robust MCVaR portfolio optimizer with ellipsoidal support and RKHS uncertainty, a PPO‑based adaptive weighting system for LLM‑generated alphas, an empirical comparison of price‑based, GICS‑based, and LLM‑embedding stock clustering, and a diffusion‑model approach that generates future financial chart images from current charts and text prompts.

Large Language Modelsdiffusion modelsportfolio optimization
0 likes · 9 min read
Weekly Quantitative Finance Paper Digest (Aug 30 – Sep 5, 2025)
Data Party THU
Data Party THU
Sep 4, 2025 · Artificial Intelligence

Unraveling PPO Variants: From GRPO to DAPO and GSPO – A Deep Dive

This article provides a comprehensive technical analysis of PPO‑based reinforcement learning methods for large language models, detailing the evolution from the original PPO algorithm through GRPO, DAPO, and GSPO, and explaining their motivations, mathematical formulations, advantages, and practical challenges such as entropy collapse and importance‑sampling variance.

DAPOGRPOGSPO
0 likes · 30 min read
Unraveling PPO Variants: From GRPO to DAPO and GSPO – A Deep Dive
Sohu Tech Products
Sohu Tech Products
Sep 3, 2025 · Artificial Intelligence

How GRPO Revolutionizes RLHF for Large Language Models

This article explains the motivation, mathematical foundations, implementation details, advantages, experimental results, and future directions of Group Relative Policy Optimization (GRPO), a novel reinforcement‑learning algorithm that replaces PPO’s value network with efficient group‑wise relative evaluation for large language models.

Artificial IntelligenceGRPOLLM
0 likes · 17 min read
How GRPO Revolutionizes RLHF for Large Language Models
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 3, 2025 · Artificial Intelligence

Decoding TINs: Reconstructing Classic Technical Analysis with Neural Networks

The paper introduces Technical Indicator Networks (TINs), a framework that maps traditional technical analysis formulas to neural‑network topologies, initializes weights to preserve indicator behavior, and uses reinforcement learning for dynamic optimization, achieving significantly higher Sharpe, Sortino, and cumulative returns on US30 component stocks than conventional MACD approaches.

Algorithmic TradingFinancial AITechnical Indicator Networks
0 likes · 9 min read
Decoding TINs: Reconstructing Classic Technical Analysis with Neural Networks
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 3, 2025 · Artificial Intelligence

How Atom-Searcher Boosts LLM Reasoning with Atomic Thought Rewards

Atom-Searcher introduces an atomic‑thought reinforcement‑learning framework that decomposes complex reasoning into fine‑grained units, uses a Reasoning Reward Model to assign step‑wise rewards, dynamically balances process and result incentives, and achieves state‑of‑the‑art performance on multiple LLM benchmarks.

Agentic ResearchAtomic ThoughtLLM
0 likes · 12 min read
How Atom-Searcher Boosts LLM Reasoning with Atomic Thought Rewards
Data STUDIO
Data STUDIO
Sep 2, 2025 · Artificial Intelligence

Understanding NAS: Core Algorithms and Python Implementations

This article reviews Neural Architecture Search (NAS), explains its bi‑level optimization formulation, compares three major search strategies—reinforcement learning, evolutionary algorithms, and differentiable gradient‑based methods—provides complete Python code for each, and analyzes experimental results highlighting performance trade‑offs and remaining challenges.

Differentiable Architecture SearchEvolutionary AlgorithmsNAS
0 likes · 25 min read
Understanding NAS: Core Algorithms and Python Implementations
Data Party THU
Data Party THU
Aug 30, 2025 · Artificial Intelligence

Understanding Multi‑Armed Bandits: Balancing Exploration and Exploitation in Reinforcement Learning

Multi‑armed bandit models illustrate the core exploration‑exploitation dilemma in reinforcement learning, covering greedy, ε‑greedy, and optimistic‑initial‑value strategies, as well as sample‑average and incremental Q‑value estimation methods with practical examples and visual illustrations.

Q-value estimationexploration vs exploitationgreedy
0 likes · 15 min read
Understanding Multi‑Armed Bandits: Balancing Exploration and Exploitation in Reinforcement Learning
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Aug 29, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)

This digest summarizes nine recent arXiv papers covering quantum portfolio optimization, thematic investing with semantic stock representations, multi‑indicator reinforcement learning for trading, attention‑based asset pricing, ESG variable selection, deep neural networks for return distribution forecasting, a foundation model for financial time‑series, a multi‑agent trading system with self‑reflection, and dynamic weighting machine‑learning stock selection strategies.

ESGdeep learningfinancial time series
0 likes · 17 min read
Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Aug 27, 2025 · Artificial Intelligence

Perception‑R1: A Rule‑Based RL Method that Elevates Multimodal Model Vision

Perception‑R1, a post‑training framework that applies rule‑based reinforcement learning to existing multimodal LLMs, dramatically improves visual perception tasks such as grounding, OCR, counting and object detection, as demonstrated by extensive benchmarks and ablation studies.

GRPOMultimodal LLMPerception Policy
0 likes · 10 min read
Perception‑R1: A Rule‑Based RL Method that Elevates Multimodal Model Vision
Kuaishou Tech
Kuaishou Tech
Aug 23, 2025 · Artificial Intelligence

How Thyme Enables Models to Think Beyond Images with Code‑Driven Multimodal Reasoning

The Kwai Keye team presents Thyme, a novel multimodal reasoning framework that lets large language models generate and safely execute Python code for image manipulation and complex calculations, achieving significant performance gains over existing vision‑language models across perception, reasoning, and hallucination‑reduction benchmarks.

AI researchMultimodalcode generation
0 likes · 12 min read
How Thyme Enables Models to Think Beyond Images with Code‑Driven Multimodal Reasoning
Architect's Must-Have
Architect's Must-Have
Aug 22, 2025 · Artificial Intelligence

Why Multi-Agent Communication Protocols Are the Future of AI Collaboration

This article examines the limitations of single-agent AI, explains how Multi-Agent Communication Protocols (MCP) address challenges such as incomplete perception, decision conflicts, and scalability, and outlines current research, industrial applications, and future directions including edge integration and blockchain synergy.

BlockchainEdge computingcommunication protocols
0 likes · 8 min read
Why Multi-Agent Communication Protocols Are the Future of AI Collaboration
Data Thinking Notes
Data Thinking Notes
Aug 21, 2025 · Artificial Intelligence

Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning

This article distills Denny Zhou’s Stanford CS25 lecture, explaining how large language models achieve reasoning through intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation, while highlighting theoretical foundations and practical breakthroughs.

LLMchain-of-thoughtreasoning
0 likes · 18 min read
Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning
Kuaishou Tech
Kuaishou Tech
Aug 21, 2025 · Artificial Intelligence

How SeamlessFlow Doubles RL Training Throughput and Cuts Time by 62%

SeamlessFlow, an industrial‑scale reinforcement‑learning training framework released by Kuaipilot, decouples trainer and agents via a novel data‑plane, introduces a tag‑based resource scheduler, and eliminates pipeline bubbles, achieving up to 100% token‑throughput boost and 62% reduction in overall training time across large‑model RL workloads.

Distributed Trainingpipeline optimizationreinforcement learning
0 likes · 13 min read
How SeamlessFlow Doubles RL Training Throughput and Cuts Time by 62%
Kuaishou Large Model
Kuaishou Large Model
Aug 19, 2025 · Artificial Intelligence

How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO

Klear-Reasoner, built on Qwen3‑8B‑Base, introduces the Gradient‑Preserving Clipping Policy Optimization (GPPO) algorithm to overcome traditional clip limitations, achieving state‑of‑the‑art performance on AIME2024/2025 and LiveCodeBench while providing detailed experimental analysis and data‑quality insights.

GPPOGradient ClippingLarge Language Models
0 likes · 11 min read
How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO
AntTech
AntTech
Aug 19, 2025 · Artificial Intelligence

How UI‑Venus Achieves SOTA in Multimodal GUI Agent Benchmarks

Ant Group's open‑source native GUI agent UI‑Venus leverages multimodal large‑model and reinforcement‑learning techniques to outperform prior models on grounding and navigation benchmarks, while using a high‑quality data pipeline and a self‑evolving alignment mechanism to push the limits of GUI automation.

GUI AgentSOTAbenchmark
0 likes · 7 min read
How UI‑Venus Achieves SOTA in Multimodal GUI Agent Benchmarks
AI Info Trend
AI Info Trend
Aug 19, 2025 · Industry Insights

What’s Driving the AI Revolution in 2025? Key Trends and Insights

The 2025 H1 AI Core Achievements and Trends report reveals how agents are reshaping productivity, models are gaining inference power and becoming smaller, reinforcement learning is overtaking pre‑training, and industry competition is intensifying, with China and the US narrowing their technology gap.

AIAgentsChina
0 likes · 10 min read
What’s Driving the AI Revolution in 2025? Key Trends and Insights
Kuaishou Tech
Kuaishou Tech
Aug 18, 2025 · Artificial Intelligence

How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO Optimization

The Klear‑Reasoner model, built on Qwen3‑8B‑Base and powered by the novel Gradient‑Preserving Clipping Policy Optimization (GPPO) algorithm, surpasses same‑size open‑source baselines on challenging math (AIME) and code (LiveCodeBench) benchmarks, while revealing key insights on data quality, reward design, and clipping strategies for large‑language‑model reasoning.

GPPOLLMcode reasoning
0 likes · 11 min read
How Klear-Reasoner Achieves SOTA Math & Code Reasoning with GPPO Optimization
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 15, 2025 · Artificial Intelligence

Unlocking LLM Performance: Classic Deep RL Tricks Reimagined for Modern Training

This article systematically adapts classic deep reinforcement‑learning techniques—such as multi‑step returns, TD(λ)/GAE, V‑trace corrections, uncertainty‑aware weighting, safety constraints, distribution‑robust optimization, and value‑guided decoding—to improve large language model training and inference, providing concrete formulas, implementation tips, and empirical results.

Deep RLGAELLM
0 likes · 17 min read
Unlocking LLM Performance: Classic Deep RL Tricks Reimagined for Modern Training
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 14, 2025 · Artificial Intelligence

Why Standard SFT Fails to Generalize and How One‑Line Dynamic Fine‑Tuning Fixes It

The article analyzes the poor generalization of supervised fine‑tuning (SFT) for large language models, reveals its gradient as a high‑variance inverse‑probability policy gradient, proposes a one‑line Dynamic Fine‑Tuning correction, and shows substantial gains on challenging math and offline RL benchmarks.

Dynamic Fine-TuningLLM alignmentSupervised Fine‑Tuning
0 likes · 7 min read
Why Standard SFT Fails to Generalize and How One‑Line Dynamic Fine‑Tuning Fixes It
AIWalker
AIWalker
Aug 13, 2025 · Artificial Intelligence

Look-Back Triggers Visual Reflection in Qwen-2.5-VL, +6.3% Perception

Look-Back is an implicit training paradigm that enables the Qwen‑2.5‑VL‑7B multimodal LLM to autonomously re‑focus on visual inputs during reasoning, achieving a 6.3 % boost in perception tasks, outperforming prior baselines while requiring no extra image tokens or model architecture changes.

Look-BackMultimodal LLMQwen-2.5-VL
0 likes · 26 min read
Look-Back Triggers Visual Reflection in Qwen-2.5-VL, +6.3% Perception
Kuaishou Tech
Kuaishou Tech
Aug 6, 2025 · Artificial Intelligence

How Supervised Learning‑Enhanced Multi‑Group Actor‑Critic Boosts Live Stream Allocation in Short‑Video Feeds

This article presents the SL‑MGAC framework, a supervised‑learning‑enhanced multi‑group Actor‑Critic algorithm that improves live‑stream insertion decisions in mixed short‑video and live‑stream recommendation systems, achieving higher stability and better long‑term user engagement while satisfying platform constraints, as validated by extensive offline and online experiments.

KDD 2025Supervised Learningactor-critic
0 likes · 9 min read
How Supervised Learning‑Enhanced Multi‑Group Actor‑Critic Boosts Live Stream Allocation in Short‑Video Feeds
AIWalker
AIWalker
Aug 5, 2025 · Artificial Intelligence

Perception‑R1: RL Gives Visual Insight Without Chain‑of‑Thought, Beats Four Tasks

The paper introduces Perception‑R1, a rule‑based reinforcement‑learning framework that trains multimodal large language models for visual perception tasks without relying on chain‑of‑thought reasoning, and demonstrates up to 17.9% performance gains on RefCOCO+, PixMo‑Count, PageOCR and COCO2017, while analyzing the key roles of perception confusion and reward design.

Multimodal LLMRLHFReward Design
0 likes · 24 min read
Perception‑R1: RL Gives Visual Insight Without Chain‑of‑Thought, Beats Four Tasks
AI Info Trend
AI Info Trend
Aug 4, 2025 · Industry Insights

How AI Agents and Small Models Are Redefining Productivity in 2025 H1

The report analyzes first‑half‑2025 AI breakthroughs, covering the rise of general‑purpose agents, rapid inference improvements, small‑model proliferation, reinforcement‑learning compute dominance, evolving transformer architectures, and shifting industry dynamics, offering actionable insights for researchers, product leaders, and decision‑makers.

AIAgentMultimodal
0 likes · 9 min read
How AI Agents and Small Models Are Redefining Productivity in 2025 H1
JD Tech
JD Tech
Jul 29, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article describes a QCon talk that combines causal inference with large language models to build a retrieval‑augmented generation pricing system for e‑commerce, detailing the three‑step algorithm, LLM‑driven modeling challenges, process‑reward tree search, reinforcement‑learning fine‑tuning, and experimental gains in accuracy and speed.

Large Language ModelsRetrieval-Augmented Generationcausal inference
0 likes · 17 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
AI Algorithm Path
AI Algorithm Path
Jul 27, 2025 · Artificial Intelligence

Understanding RLHF: How Human Feedback Trains Modern LLMs

This article explains the RLHF (Reinforcement Learning from Human Feedback) pipeline that powers ChatGPT and other large language models, covering the limitations of traditional fine‑tuning, the creation of human‑feedback datasets, reward‑model training, loss design, and the final PPO‑based fine‑tuning step.

ChatGPTHuman FeedbackLarge Language Models
0 likes · 8 min read
Understanding RLHF: How Human Feedback Trains Modern LLMs
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jul 24, 2025 · Artificial Intelligence

Exploring Recent Large‑Model Agent Papers: Insights and Analyses

This article reviews a series of recent research papers on large‑model agents, covering topics such as reinforcement‑learning‑driven ML agents, premise‑critique ability of LLMs, long‑term tool‑augmented LLM evaluation, agentic RAG, set‑based retrieval for multi‑hop QA, mobile VLM agents, and broader surveys of LLM applications, summarizing each work’s problem statement, prior approaches, novel contributions, experimental results, limitations, and future directions.

LLM evaluationLarge Language ModelsRetrieval-Augmented Generation
0 likes · 46 min read
Exploring Recent Large‑Model Agent Papers: Insights and Analyses
Fun with Large Models
Fun with Large Models
Jul 23, 2025 · Artificial Intelligence

Why ChatGPT Agent Sets the Benchmark for Future Large‑Model AI Agents

The article analyzes OpenAI's ChatGPT Agent—its launch, performance metrics, all‑in‑one tool integration, real‑world use cases, pricing tiers, core capabilities, and how it surpasses competing agents like Manus, highlighting its significance for the next generation of AI agents.

AI AgentChatGPT AgentPricing
0 likes · 11 min read
Why ChatGPT Agent Sets the Benchmark for Future Large‑Model AI Agents
JD Tech Talk
JD Tech Talk
Jul 23, 2025 · Artificial Intelligence

Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies

This article describes how integrating causal inference with large language models and Retrieval‑Augmented Generation can automate and explain e‑commerce product pricing, detailing the three‑step workflow, reinforcement‑learning rewards, experimental results, and future directions for end‑to‑end RAG‑LLM training.

RAGcausal inferencee‑commerce pricing
0 likes · 15 min read
Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies
JD Cloud Developers
JD Cloud Developers
Jul 23, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

At QCon 2025, the author presented a novel approach that integrates causal inference with large language models using Retrieval‑Augmented Generation, process rewards, and tree‑search to generate explainable, accurate e‑commerce pricing recommendations, dramatically improving accuracy from 44% to 74% while cutting inference time to seconds.

causal inferencee‑commerce pricingreinforcement learning
0 likes · 14 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
DataFunTalk
DataFunTalk
Jul 23, 2025 · Artificial Intelligence

Qwen3‑Coder: Open‑Source AI Programming Agent That Beats the Competition

Alibaba’s Tongyi team unveiled the open‑source Qwen3‑Coder, a massive 450‑billion‑parameter programming model that outperforms leading closed‑source solutions, supports up to 1 M token context, offers a free CLI tool, and demonstrates impressive code generation capabilities across animations, games, and real‑world tasks.

AI programmingOpen SourceQwen3-Coder
0 likes · 5 min read
Qwen3‑Coder: Open‑Source AI Programming Agent That Beats the Competition
Kuaishou Tech
Kuaishou Tech
Jul 21, 2025 · Artificial Intelligence

Can AI Models Think on Demand? Inside KAT‑V1 AutoThink’s Dynamic Reasoning

The article introduces KAT‑V1 AutoThink, a dual‑mode large language model that automatically switches between thinking and non‑thinking modes based on problem difficulty, details its novel training paradigm, reinforcement‑learning enhancements, performance benchmarks against leading open‑source models, and provides open‑source resources for further research.

auto-thinkknowledge distillationlarge language model
0 likes · 14 min read
Can AI Models Think on Demand? Inside KAT‑V1 AutoThink’s Dynamic Reasoning
JD Retail Technology
JD Retail Technology
Jul 21, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article presents a comprehensive approach that combines causal inference, large language models, and retrieval‑augmented generation to automate e‑commerce price recommendation, detailing the three‑step workflow, challenges across product categories, the RAG architecture, process‑reward‑guided tree search, reinforcement learning refinements, and experimental results showing significant accuracy and speed improvements.

causal inferencechain-of-thoughte‑commerce pricing
0 likes · 16 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
Alimama Tech
Alimama Tech
Jul 17, 2025 · Artificial Intelligence

How to Build a High‑Scoring AI Werewolf Agent: Strategies, Prompt Engineering, and Code

This article details the author's experience designing a top‑performing AI Werewolf agent for the Taotian Group's AI Werewolf Challenge, covering game rules, core challenges, prompt engineering, caching, concurrent requests, model selection, reinforcement‑learning‑style tuning, and tactical strategies for each role, with code examples.

AI AgentLLMPrompt Engineering
0 likes · 25 min read
How to Build a High‑Scoring AI Werewolf Agent: Strategies, Prompt Engineering, and Code
DataFunTalk
DataFunTalk
Jul 16, 2025 · Artificial Intelligence

MiniMax-M1 Revealed: Hybrid Attention, RL Training, and 1M Token Context

MiniMax’s latest M1 model, unveiled after a $300 million funding round, showcases a 4.56‑trillion‑parameter hybrid‑expert architecture with lightning attention, supporting up to one million tokens, and leverages reinforcement‑learning techniques to enhance long‑context handling, inference efficiency, and system‑2 reasoning capabilities.

AI scalingHybrid AttentionLarge Language Models
0 likes · 16 min read
MiniMax-M1 Revealed: Hybrid Attention, RL Training, and 1M Token Context
AI Algorithm Path
AI Algorithm Path
Jul 14, 2025 · Artificial Intelligence

The Most Powerful Open‑Source Agent Model: Kimi K2

Kimi K2, an open‑source trillion‑parameter AI model released by Moonshot AI, offers Base and Instruct variants, achieves leading scores on benchmarks such as SWE‑bench, LiveCodeBench and AceBench, and introduces a novel post‑training autonomous‑exploration stage with MuonClip optimization to enable robust tool use and reinforcement‑learning‑driven self‑improvement.

Autonomous AgentsKimi K2Open source AI
0 likes · 8 min read
The Most Powerful Open‑Source Agent Model: Kimi K2
AI Frontier Lectures
AI Frontier Lectures
Jul 14, 2025 · Artificial Intelligence

Can Language Models Self‑Edit? Inside SEAL’s Self‑Adapting LLM Framework

The article surveys recent AI self‑evolution research, highlights the SEAL self‑adapting language model framework, explains its reinforcement‑learning based self‑editing mechanism, and presents experimental results on few‑shot learning and knowledge integration, while noting limitations and providing links to the paper and code.

AI self‑improvementSEALmeta‑learning
0 likes · 12 min read
Can Language Models Self‑Edit? Inside SEAL’s Self‑Adapting LLM Framework
Python Programming Learning Circle
Python Programming Learning Circle
Jul 10, 2025 · Artificial Intelligence

Build a DQN Autonomous Driving Agent with gym and highway‑env

This tutorial walks through installing gym and highway‑env, configuring six driving scenarios, processing observations (kinematics, images, occupancy grids), defining actions and rewards, constructing a DQN network, training it with a reinforcement‑learning loop, and analyzing collision, time, and reward metrics.

DQNautonomous drivinggym
0 likes · 10 min read
Build a DQN Autonomous Driving Agent with gym and highway‑env
Data Thinking Notes
Data Thinking Notes
Jul 8, 2025 · Artificial Intelligence

How Xiaohongshu Leverages Large Models to Revolutionize Content Recommendation

This article details Xiaohongshu's multi‑stage recommendation pipeline—using massive multi‑modal pre‑training, long‑sequence modeling, real‑time context features, reinforcement learning and online deep learning—to precisely surface valuable content, address cold‑start challenges, and break information bubbles for billions of users.

large language modelmultimodal learningonline deep learning
0 likes · 16 min read
How Xiaohongshu Leverages Large Models to Revolutionize Content Recommendation
DataFunSummit
DataFunSummit
Jul 5, 2025 · Artificial Intelligence

Boosting Large Model Training: Optimizing Performance with the Verl Framework

Join the DataFun Summit 2025 on July 12 to hear Tencent FinTech senior researcher Gong Dihong discuss how redesigning the Verl training system, integrating Megatron and Sglang, and applying new synchronization and offloading techniques dramatically speeds up large‑model reinforcement‑learning training.

AI performanceMegatronTraining Optimization
0 likes · 4 min read
Boosting Large Model Training: Optimizing Performance with the Verl Framework
AI Frontier Lectures
AI Frontier Lectures
Jul 2, 2025 · Artificial Intelligence

Can Language Models Self‑Edit? Inside the SEAL Framework for Self‑Adapting LLMs

This article reviews recent AI self‑evolution research and provides an in‑depth analysis of the SEAL (Self‑Adapting Language) framework, which enables large language models to generate and learn from their own synthetic data through a nested reinforcement‑learning and fine‑tuning loop, with experimental results on few‑shot and knowledge‑integration tasks.

Knowledge IntegrationLarge Language ModelsSEAL
0 likes · 11 min read
Can Language Models Self‑Edit? Inside the SEAL Framework for Self‑Adapting LLMs
DataFunTalk
DataFunTalk
Jul 2, 2025 · Artificial Intelligence

How GLM-4.1V-Thinking Sets New Standards in Multimodal AI Reasoning

Zhipu AI unveiled the GLM-4.1V-Thinking series, an open‑source multimodal model that outperforms larger rivals on visual‑language tasks, supports video analysis, GUI agents, and advanced scientific reasoning, while introducing a curriculum‑sampling reinforcement‑learning framework and a new Agent application platform.

AI agentsGLM-4.1VOpen Source
0 likes · 10 min read
How GLM-4.1V-Thinking Sets New Standards in Multimodal AI Reasoning
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 30, 2025 · Artificial Intelligence

How End‑to‑End Reinforcement Learning Powers the Kimi‑Researcher AI Agent

The article examines Kimi‑Researcher, an AI research agent built with end‑to‑end reinforcement learning, detailing its technical motivations, advantages over traditional workflow‑based and SFT methods, performance breakthroughs on benchmark exams, and diverse real‑world use cases ranging from literature reviews to legal analysis.

AI AgentEnd-to-End RLKimi Researcher
0 likes · 10 min read
How End‑to‑End Reinforcement Learning Powers the Kimi‑Researcher AI Agent
Hailey Says
Hailey Says
Jun 29, 2025 · Artificial Intelligence

If Life Were an RLHF, Who’s Shaping Your Rewards?

The article explains the three‑stage RLHF pipeline—pretraining, supervised fine‑tuning, and reward‑model reinforcement—and draws a detailed analogy to human life phases, showing how early data, personal values, and continual feedback act as a reward function that can be consciously re‑engineered.

AI AlignmentHuman FeedbackLarge Language Models
0 likes · 13 min read
If Life Were an RLHF, Who’s Shaping Your Rewards?
Fighter's World
Fighter's World
Jun 28, 2025 · Artificial Intelligence

What Is the Generator‑Verifier Gap and Why It Matters for LLM Reasoning

The article explains the Generator‑Verifier Gap (GVG)—the asymmetry where verifying a solution is far cheaper than generating it—covers its origin, its impact on test‑time scaling for large language models, reinforcement‑learning approaches, and how the concept can shape agent architectures and AI product strategy.

Agent ArchitectureGenerator-Verifier GapLLM
0 likes · 21 min read
What Is the Generator‑Verifier Gap and Why It Matters for LLM Reasoning
Alimama Tech
Alimama Tech
Jun 25, 2025 · Artificial Intelligence

Introducing ROLL: A Scalable, User‑Friendly RL Framework for Large‑Scale LLM Training

ROLL is an open‑source reinforcement‑learning framework designed for large language model post‑training that combines multi‑task RL, agentic support, flexible algorithm configuration, elastic resource scheduling, and rich observability, delivering significant accuracy gains across benchmarks while remaining easy to use for researchers, product developers, and infrastructure engineers.

AI FrameworkLarge Language ModelsOpen Source
0 likes · 11 min read
Introducing ROLL: A Scalable, User‑Friendly RL Framework for Large‑Scale LLM Training
DataFunTalk
DataFunTalk
Jun 21, 2025 · Artificial Intelligence

Why AI Gets Overconfident: Bias, Hallucinations, and Reinforcement Learning Solutions

This talk explores how large AI models become overconfident, leading to bias and hallucinations, examines adversarial examples in vision and language, explains why data and algorithms cause these issues, and shows how reinforcement learning can teach models to admit uncertainty and align with human values.

AI AlignmentAI safetyBias
0 likes · 19 min read
Why AI Gets Overconfident: Bias, Hallucinations, and Reinforcement Learning Solutions
Kuaishou Large Model
Kuaishou Large Model
Jun 20, 2025 · Artificial Intelligence

How OneRec Revolutionizes Short-Video Recommendations with End-to-End Generative AI

OneRec, an end-to-end generative recommendation system from Kuaishou, uses an encoder-decoder architecture, reward-based preference alignment, and reinforcement learning to dramatically improve video recommendation efficiency, boosting user engagement and reducing operational costs while achieving scaling-law performance comparable to large language models.

EfficiencyKuaishougenerative AI
0 likes · 18 min read
How OneRec Revolutionizes Short-Video Recommendations with End-to-End Generative AI
Kuaishou Tech
Kuaishou Tech
Jun 20, 2025 · Artificial Intelligence

How OneRec Redefines Recommendation with End‑to‑End Generative Modeling and RL Alignment

The OneRec system from Kuaishou replaces traditional cascade recommendation pipelines with an encoder‑decoder architecture, leverages reward‑based preference alignment via reinforcement learning, achieves ten‑fold FLOPs gains, cuts operational costs by 90%, and delivers significant user‑engagement improvements across short‑video and local‑service scenarios.

Generative ModelingKuaishouOneRec
0 likes · 17 min read
How OneRec Redefines Recommendation with End‑to‑End Generative Modeling and RL Alignment
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Jun 19, 2025 · Artificial Intelligence

Can Adaptive Chain‑of‑Thought Learning Halve LLM Thinking Time?

The article introduces the Think When You Need (TWYN) method, a reinforcement‑learning approach that dynamically adapts chain‑of‑thought length, dramatically cuts redundant token generation in large language models, and maintains or improves accuracy across diverse reasoning benchmarks.

EfficiencyLarge Language Modelsadaptive inference
0 likes · 9 min read
Can Adaptive Chain‑of‑Thought Learning Halve LLM Thinking Time?
DataFunTalk
DataFunTalk
Jun 17, 2025 · Artificial Intelligence

Kimi-Dev-72B Sets New Open‑Source SOTA on SWE‑bench Verified (60.4% Score)

Kimi-Dev-72B, an open-source 72-billion-parameter code model from Moonshot AI, achieved a record 60.4% score on the SWE-bench Verified benchmark, surpassing larger models, and incorporates BugFixer/TestWriter dual roles, extensive mid-stage training on billions of GitHub data, and reinforcement-learning-driven self-play, with code available on Hugging Face and GitHub.

AISWE-benchSoftware Engineering
0 likes · 7 min read
Kimi-Dev-72B Sets New Open‑Source SOTA on SWE‑bench Verified (60.4% Score)
Fighter's World
Fighter's World
Jun 14, 2025 · Artificial Intelligence

How Can LLMs Learn to “Think” in Complex Industry Scenarios?

The article analyzes how large language models can acquire true reasoning abilities for hard‑to‑score industry tasks by combining Chain‑of‑Thought prompting with reinforcement learning, addressing vague reward signals, reward hacking, and loyalty, and proposing a toolbox of reward engineering, synthetic data, hierarchical RL and multi‑agent collaboration.

LLMchain-of-thoughtmulti-agent systems
0 likes · 22 min read
How Can LLMs Learn to “Think” in Complex Industry Scenarios?
Fun with Large Models
Fun with Large Models
Jun 12, 2025 · Artificial Intelligence

Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B

This article explains the GRPO reinforcement‑learning algorithm, shows its core idea of internal group competition without a separate evaluator model, and provides a complete, step‑by‑step code walkthrough—including environment setup, dataset preparation, reward‑function design, training configuration, and evaluation—using the Qwen2.5‑0.5B‑Instruct model on the GSM8K math dataset.

GRPOGSM8KQwen2.5
0 likes · 23 min read
Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B
Kuaishou Tech
Kuaishou Tech
Jun 4, 2025 · Artificial Intelligence

KwaiCoder-AutoThink-preview: An Automatic‑Thinking Large Model Enhanced with Step‑SRPO Reinforcement Learning

The KwaiPilot team released the KwaiCoder‑AutoThink‑preview model, which introduces a novel automatic‑thinking training paradigm and a process‑supervised reinforcement‑learning method called Step‑SRPO, enabling the model to dynamically switch between thinking and non‑thinking modes, reduce inference cost, and achieve up to 20‑point gains on code and math benchmarks while handling large‑scale codebases.

AI researchModel Optimizationautomatic thinking
0 likes · 12 min read
KwaiCoder-AutoThink-preview: An Automatic‑Thinking Large Model Enhanced with Step‑SRPO Reinforcement Learning
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 3, 2025 · Artificial Intelligence

Can 1K Fine‑Tuning Replace 100K RL Steps? Insights from Re‑distillation Research

An extensive analysis shows that a 1K‑sample fine‑tuning stage can replicate the generalization gains of thousands of reinforcement‑learning steps, explains the compressibility of RL, introduces a sample‑effect theory, and demonstrates that re‑distillation and small‑scale SFT dramatically improve LLM performance.

Large Language ModelsRe-distillationSample Effect
0 likes · 23 min read
Can 1K Fine‑Tuning Replace 100K RL Steps? Insights from Re‑distillation Research
AI Frontier Lectures
AI Frontier Lectures
May 31, 2025 · Artificial Intelligence

Why Embodied Intelligence Is Exploding and What It Means for the Future

The article analyzes the recent surge in embodied intelligence, examines why physical agents matter despite advances in large language models, outlines common failure modes, discusses key research decisions such as 2D versus 3D perception and tactile sensing, and explores the roles of imitation learning, VLA, and reinforcement learning in shaping the field.

VLAVisionimitation learning
0 likes · 24 min read
Why Embodied Intelligence Is Exploding and What It Means for the Future
AI Frontier Lectures
AI Frontier Lectures
May 30, 2025 · Artificial Intelligence

Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?

Recent work from West Lake University's MAPLE Lab introduces a diffusion‑based “Divergent Thought Chain” that treats each intermediate denoising step of a diffusion language model as a reasoning step, using result‑based reinforcement learning to optimize non‑linear token generation and achieving state‑of‑the‑art performance on math and code tasks.

chain-of-thoughtcode generationdiffusion language models
0 likes · 14 min read
Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?
Alibaba Cloud Developer
Alibaba Cloud Developer
May 28, 2025 · Artificial Intelligence

Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment

This article provides a comprehensive guide to large language model fine‑tuning, covering model architecture, parameter and memory calculations, prompt engineering, data construction, LoRA and PEFT techniques, reinforcement learning methods such as DPO, and practical deployment workflows on internal platforms.

Fine‑TuningLLMLoRA
0 likes · 21 min read
Unlocking LLM Fine‑Tuning: From Architecture to LoRA, DPO and Deployment
JD Cloud Developers
JD Cloud Developers
May 27, 2025 · Artificial Intelligence

How JD’s Young AI Engineers Tackle Real-World Model Challenges

Young JD algorithm engineers share how they solve tough AI problems—from optimizing large‑model training and reward‑model design for ad image generation, to building LLM‑based query expansion, agent evaluation, and model pruning with FFT and RDP—illustrating practical breakthroughs and personal growth in cutting‑edge AI research.

AIAlgorithm EngineeringLarge Language Models
0 likes · 15 min read
How JD’s Young AI Engineers Tackle Real-World Model Challenges
AI Algorithm Path
AI Algorithm Path
May 27, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization

This tutorial explains how to construct state feature vectors for reinforcement‑learning value‑function approximation, covering linear, polynomial, Fourier, and radial‑basis representations, as well as state aggregation techniques such as coarse coding and tile coding, and discusses non‑parametric approaches like kernel methods.

feature engineeringfourier basisfunction approximation
0 likes · 16 min read
Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization
AIWalker
AIWalker
May 26, 2025 · Artificial Intelligence

VisionReasoner: RL‑Unified Model Beats YOLO‑World Detection, Segmentation, Counting

VisionReasoner presents a reinforcement‑learning‑driven unified framework that simultaneously tackles detection, segmentation, and counting tasks, employing a novel multi‑target cognition strategy and efficient Hungarian‑based matching, and demonstrates substantial gains—29.1% on COCO detection, 22.1% on ReasonSeg, and 15.3% on CountBench—using only 7,000 training samples.

SegmentationVisionReasonercounting
0 likes · 20 min read
VisionReasoner: RL‑Unified Model Beats YOLO‑World Detection, Segmentation, Counting
JD Tech
JD Tech
May 26, 2025 · Artificial Intelligence

Solving Technical Challenges at JD Retail: Multi‑Reward Models, LLM‑Based Query Expansion, Model Pruning, and Reinforcement Learning

This article details how JD Retail's young algorithm engineers tackled a series of AI engineering problems—including advertising image quality assessment with multi‑reward models, large‑language‑model‑driven query expansion, FFT‑and‑RDP‑based model pruning, and agent‑centric reinforcement learning—while sharing practical growth insights and code snippets.

AILarge Language ModelsModel Optimization
0 likes · 15 min read
Solving Technical Challenges at JD Retail: Multi‑Reward Models, LLM‑Based Query Expansion, Model Pruning, and Reinforcement Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
May 26, 2025 · Artificial Intelligence

How Multi‑Agent Planning Boosts Copilot 3.0 with DeepSeek R1 GRPO Training

This article examines Copilot 3.0’s planning module, explains how DeepSeek R1’s GRPO reinforcement‑learning pipeline enables flexible multi‑agent orchestration, addresses the limitations of Copilot 2.0, and presents experimental results that show a 61% reduction in reasoning length and a 9% relative gain in accuracy.

AIReward Designmodel training
0 likes · 14 min read
How Multi‑Agent Planning Boosts Copilot 3.0 with DeepSeek R1 GRPO Training
AI Algorithm Path
AI Algorithm Path
May 25, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial 7: Introducing Value Function Approximation Methods

This article explains why tabular reinforcement‑learning methods scale poorly, introduces supervised‑learning‑based value‑function approximation using a parameterized vector w, discusses loss design, stochastic‑gradient updates, bootstrapping, semi‑gradient techniques, and linear function approximation, and summarizes practical implications.

Supervised Learninggradient Monte Carlolinear function approximation
0 likes · 13 min read
Reinforcement Learning Tutorial 7: Introducing Value Function Approximation Methods
IT Services Circle
IT Services Circle
May 25, 2025 · Artificial Intelligence

DeepSeek Core Technologies and Model Innovations: DeepSeek‑V3 and DeepSeek‑R1 Technical Overview

The article provides a detailed technical overview of DeepSeek's flagship large language models, DeepSeek‑V3 and DeepSeek‑R1, describing their MoE architecture, training frameworks, reinforcement‑learning based fine‑tuning, inference optimizations, and the broader impact of these innovations on the AI landscape while also promoting related books and resources.

AIDeepSeekMixture of Experts
0 likes · 10 min read
DeepSeek Core Technologies and Model Innovations: DeepSeek‑V3 and DeepSeek‑R1 Technical Overview
AI Algorithm Path
AI Algorithm Path
May 24, 2025 · Artificial Intelligence

How N-step Temporal-Difference Methods Extend TD Learning in Reinforcement AI

This tutorial explains how n-step temporal‑difference (TD) algorithms generalize the one‑step TD and Monte‑Carlo methods, presents the n‑step return update rule, walks through a three‑step TD example, shows how Sarsa and Q‑learning can be extended, and discusses how to choose the optimal n value for a given problem.

Monte CarloQ-Learningalgorithm analysis
0 likes · 9 min read
How N-step Temporal-Difference Methods Extend TD Learning in Reinforcement AI
AI Algorithm Path
AI Algorithm Path
May 23, 2025 · Artificial Intelligence

Understanding Temporal‑Difference Algorithms in Reinforcement Learning

This tutorial explains temporal‑difference (TD) learning, compares it with dynamic programming and Monte‑Carlo methods, walks through concrete soccer‑match examples, shows one‑step TD versus constant‑α Monte‑Carlo updates, discusses convergence, bias, and introduces popular TD variants such as Sarsa, Q‑learning, Expected Sarsa and double learning.

Monte CarloQ-LearningTD learning
0 likes · 18 min read
Understanding Temporal‑Difference Algorithms in Reinforcement Learning
AI Algorithm Path
AI Algorithm Path
May 22, 2025 · Artificial Intelligence

Monte Carlo Policy Improvement in RL: Epsilon‑Greedy, On‑Policy vs Off‑Policy, and Incremental Updates

This tutorial explains how Monte Carlo methods are enhanced in reinforcement learning through epsilon‑greedy and epsilon‑soft policies, Monte Carlo control, a Blackjack Q‑function example, the distinction between on‑policy and off‑policy learning, importance sampling, and efficient incremental update techniques.

Epsilon-GreedyImportance SamplingIncremental Update
0 likes · 14 min read
Monte Carlo Policy Improvement in RL: Epsilon‑Greedy, On‑Policy vs Off‑Policy, and Incremental Updates
AIWalker
AIWalker
May 22, 2025 · Artificial Intelligence

VisionReasoner: RL‑Unified System Beats YOLO‑World on Detection, Segmentation, Counting

VisionReasoner introduces a reinforcement‑learning‑driven unified framework that simultaneously handles detection, segmentation, and counting tasks within a single model, achieving 29.1% higher COCO detection AP, 22.1% better ReasonSeg segmentation, and 15.3% improvement on CountBench, while requiring only 7,000 training samples and offering efficient multi‑target matching via batch computation and the Hungarian algorithm.

LVLMObject CountingVisionReasoner
0 likes · 19 min read
VisionReasoner: RL‑Unified System Beats YOLO‑World on Detection, Segmentation, Counting
JD Tech Talk
JD Tech Talk
May 22, 2025 · Artificial Intelligence

From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection

The article recounts Xiaoting’s journey from a PhD research background to leading JD.com’s ad‑fraud detection, detailing how large language models, reinforcement learning, and model distillation were applied to identify hidden address codes, reduce false‑positive rates to 0.3%, and balance accuracy with real‑time performance in a high‑traffic e‑commerce environment.

AIAd FraudAdvertising
0 likes · 11 min read
From Academic Research to Industrial Anti‑Fraud: Leveraging LLMs, Reinforcement Learning, and Model Distillation for Advertising Risk Detection
JD Retail Technology
JD Retail Technology
May 22, 2025 · Industry Insights

Cracking Hidden Ad Fraud: JD’s AI‑Driven Anti‑Cheat System Explained

This article recounts the journey of a JD PhD trainee who transformed academic research on anomaly detection into a production‑grade, LLM‑enhanced anti‑fraud system that identifies concealed address codes in CPS ads, detailing model design, LoRA fine‑tuning, reinforcement learning, distillation, cost‑aware deployment, and lessons learned for scalable ad risk management.

ad fraud detectionindustry AIlarge language model
0 likes · 12 min read
Cracking Hidden Ad Fraud: JD’s AI‑Driven Anti‑Cheat System Explained
AI Algorithm Path
AI Algorithm Path
May 19, 2025 · Artificial Intelligence

Understanding Policy Evaluation and Improvement in Reinforcement Learning

This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.

Bellman equationGridWorldgeneralized policy iteration
0 likes · 15 min read
Understanding Policy Evaluation and Improvement in Reinforcement Learning
Amap Tech
Amap Tech
May 19, 2025 · Artificial Intelligence

Group Policy Gradient: Direct Objective Optimization for Faster Reinforcement Learning

The article introduces Group Policy Gradient (GPG), a reinforcement‑learning framework that eliminates surrogate loss functions and critic models, directly optimizes the original objective, reduces bias and variance, and achieves state‑of‑the‑art performance on both single‑modal and multimodal tasks.

AI researchLLM fine-tuningbias reduction
0 likes · 7 min read
Group Policy Gradient: Direct Objective Optimization for Faster Reinforcement Learning
AI Algorithm Path
AI Algorithm Path
May 18, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial Part 1: Core Concepts Explained

This article introduces the fundamental concepts of reinforcement learning, covering the agent‑environment interaction, key terminology, reward structures, task types, policies, value functions, the Bellman equations, and how optimal strategies are derived and approximated in practice.

Bellman equationMarkov Decision ProcessOptimal Policy
0 likes · 13 min read
Reinforcement Learning Tutorial Part 1: Core Concepts Explained
Kuaishou Tech
Kuaishou Tech
May 13, 2025 · Artificial Intelligence

How KuaiMod Uses Multimodal AI to Revolutionize Short‑Video Content Quality

This article analyzes KuaiMod, a multimodal large‑model solution developed by Kuaishou for short‑video content quality assessment, detailing its benchmark dataset, chain‑of‑thought data construction, offline SFT + DPO training, online reinforcement‑learning updates, evaluation results, and large‑scale deployment impact.

KuaiModbenchmarkcontent moderation
0 likes · 19 min read
How KuaiMod Uses Multimodal AI to Revolutionize Short‑Video Content Quality
AI Frontier Lectures
AI Frontier Lectures
May 13, 2025 · Artificial Intelligence

How T2I‑R1 Boosts Text‑to‑Image Generation with Dual‑Level CoT Reasoning

Recent large language models have shown strong reasoning abilities, and this work extends chain‑of‑thought reasoning to autoregressive image generation by introducing T2I‑R1, a dual‑level (Semantic‑CoT and Token‑CoT) framework trained with reinforcement learning that unifies high‑level planning and low‑level token generation, achieving state‑of‑the‑art results.

generative AIreinforcement learningsemantic planning
0 likes · 7 min read
How T2I‑R1 Boosts Text‑to‑Image Generation with Dual‑Level CoT Reasoning
AI Frontier Lectures
AI Frontier Lectures
May 13, 2025 · Artificial Intelligence

How Diffusion Policy is Transforming Vision‑Based Robot Motion Learning

This article provides a comprehensive, step‑by‑step analysis of Diffusion Policy for robot visuomotor control, covering its motivation, task characteristics, model design, dataset preparation, training pipeline, inference procedure, experimental results, and open research questions.

diffusion modelsmachine learningpolicy learning
0 likes · 63 min read
How Diffusion Policy is Transforming Vision‑Based Robot Motion Learning
Tencent Technical Engineering
Tencent Technical Engineering
May 12, 2025 · Artificial Intelligence

Comprehensive Summary and Expansion of Andrej Karpathy’s 7‑Hour LLM Lecture

This article provides a detailed Chinese‑to‑English summary of Andrej Karpathy’s 7‑hour LLM tutorial, covering chat process analysis, tokenization, pre‑training data pipelines, model architecture, training strategies, post‑training fine‑tuning, reinforcement learning, chain‑of‑thought reasoning, and current industry applications.

AILLMmodel architecture
0 likes · 25 min read
Comprehensive Summary and Expansion of Andrej Karpathy’s 7‑Hour LLM Lecture
JD Retail Technology
JD Retail Technology
May 7, 2025 · Artificial Intelligence

Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning

JD Retail’s engineering team tackles hard AI problems by replacing a monolithic reward model with specialized small models for ad‑image generation, deploying an LLM‑driven query‑expansion pipeline that lifts conversion rates, and pruning text‑to‑image transformers using FFT and RDP to boost throughput 40% without loss, while building comprehensive evaluation tools and a semantic smart‑assistant.

AIModel PruningQuery Expansion
0 likes · 14 min read
Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning
AIWalker
AIWalker
May 6, 2025 · Artificial Intelligence

SimpleAR: High‑Quality 1024×1024 Images with Just 0.5B Parameters via Pretraining, SFT, and RL

SimpleAR demonstrates that a vanilla autoregressive model with only 0.5 B parameters can generate high‑fidelity 1024×1024 images, covering pretraining, supervised fine‑tuning, and reinforcement learning, achieving competitive GenEval (0.59) and DPG‑Bench (79.66) scores while reducing inference time to about 14 seconds with vLLM and KV‑cache optimizations.

Supervised Fine‑Tuningautoregressivebenchmark
0 likes · 14 min read
SimpleAR: High‑Quality 1024×1024 Images with Just 0.5B Parameters via Pretraining, SFT, and RL
DevOps
DevOps
May 5, 2025 · Artificial Intelligence

DeepSeek Releases Math‑Specialized Large Model V2 and ProverBench Evaluation Suite

DeepSeek has quietly open‑sourced a new mathematics‑focused large language model, DeepSeek‑Prover‑V2 (available in 671B and 7B variants), achieving 88.9% on MiniF2F and strong results on PutnamBench, alongside the high‑quality ProverBench dataset and a novel recursive theorem‑proving pipeline.

AIDeepSeekMathematical Reasoning
0 likes · 4 min read
DeepSeek Releases Math‑Specialized Large Model V2 and ProverBench Evaluation Suite
Architect
Architect
May 5, 2025 · Artificial Intelligence

How Agentic RAG‑R1 Turns Retrieval‑Augmented Generation into an Autonomous AI Agent

Agentic RAG‑R1, an open‑source project from Peking University, combines Retrieval‑Augmented Generation with an agentic AI loop, introduces the GRPO reinforcement‑learning optimizer, supports LoRA‑based fine‑tuning, quantization and multimodal tool calls, and demonstrates significant accuracy gains on the MedQA benchmark across both Chinese and English test sets.

LLM Tool UseLarge Language ModelsOpen Source
0 likes · 8 min read
How Agentic RAG‑R1 Turns Retrieval‑Augmented Generation into an Autonomous AI Agent
AI Frontier Lectures
AI Frontier Lectures
May 5, 2025 · Industry Insights

What Will Large Language Models Look Like in the Next Five Years? A Deep Dive into Trends and Challenges

The article reviews five years of AI model evolution, analyzes current scaling and reinforcement‑learning trends, and forecasts architectural, mathematical, and infrastructure directions for large language models through 2030, highlighting potential breakthroughs and the risks of over‑reliance on benchmarks.

AI trendsLarge Language Modelsindustry analysis
0 likes · 22 min read
What Will Large Language Models Look Like in the Next Five Years? A Deep Dive into Trends and Challenges
AI Algorithm Path
AI Algorithm Path
May 3, 2025 · Artificial Intelligence

DeepSeek Prover V2: Pioneering the Next Era of AI‑Driven Formal Math Reasoning

DeepSeek‑Prover‑V2, an open‑source LLM specialized for Lean 4, bridges intuitive high‑level reasoning and strict formal verification through sub‑goal decomposition, dual operation modes, and a novel cold‑start data pipeline, achieving state‑of‑the‑art results on MiniF2F, PutnamBench and CombiBench while highlighting trade‑offs in inference cost and model scalability.

AI mathematicsDeepSeek Prover V2LLM
0 likes · 18 min read
DeepSeek Prover V2: Pioneering the Next Era of AI‑Driven Formal Math Reasoning
Baobao Algorithm Notes
Baobao Algorithm Notes
May 2, 2025 · Artificial Intelligence

Do Reinforcement Learning Techniques Really Boost LLM Reasoning? A Deep Dive into Recent Models

This article analyzes whether reinforcement learning enhances large language model reasoning, compares findings from DeepSeek-Math, a Tsinghua‑Shanghai Jiao‑Tong paper, and Qwen3, and outlines practical training pipelines—including Seed‑Thinking‑v1.5, DeepSeek‑R1, Kimi‑K1.5, and Qwen3—that aim to endow LLMs with robust reasoning capabilities.

Artificial IntelligenceLLMmodel training
0 likes · 12 min read
Do Reinforcement Learning Techniques Really Boost LLM Reasoning? A Deep Dive into Recent Models